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The proposed research introduces a methodology for the classification of multi-class skin lesions by utilizing an ensemble model that integrates the Inception-V3, ResNet-50, and VGG16 architectures. It is used to categorize skin lesions into their specific classes, such as Melanoma (MEL), Basal Cell Carcinoma (BCC), and Nevus (NEV), employing the ISIC dataset. The ensemble model, augmented with data enhancement techniques, significantly surpasses the performance of individual models in skin lesion classification, achieving superior results in precision, accuracy, recall, and F1-score for both the original and balanced datasets. This approach provides a robust framework for skin lesion classification, thereby enhancing the reliability and accuracy of the diagnostic process in the field of dermatology.

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Skin Lesions Classification Using an Ensemble Deep Learning Model

  • 会议论文

  • 发表日期：

    2026年

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  • Shubham Chaurasia,
  • Shiwangi Choudhary

摘要

The proposed research introduces a methodology for the classification of multi-class skin lesions by utilizing an ensemble model that integrates the Inception-V3, ResNet-50, and VGG16 architectures. It is used to categorize skin lesions into their specific classes, such as Melanoma (MEL), Basal Cell Carcinoma (BCC), and Nevus (NEV), employing the ISIC dataset. The ensemble model, augmented with data enhancement techniques, significantly surpasses the performance of individual models in skin lesion classification, achieving superior results in precision, accuracy, recall, and F1-score for both the original and balanced datasets. This approach provides a robust framework for skin lesion classification, thereby enhancing the reliability and accuracy of the diagnostic process in the field of dermatology.